Research Article
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Year 2024, , 59 - 74, 31.12.2024
https://doi.org/10.17093/alphanumeric.1537174

Abstract

References

  • Addiga, A., & Bagui, S. (2022). Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency. Journal of Computer and Communications, 10(8), 117–128. https://doi.org/10.4236/jcc.2022.108008
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  • Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1–31.
  • Caropreso, M. F., Matwin, S., & Sebastiani, F. (2001). A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. Text Databases and Document Management: Theory and Practice, 5478(4), 78–102.
  • Clark, J. H., Garrette, D., Turc, I., & Wieting, J. (2022). <scp>Canine</scp>: Pre-training an Efficient Tokenization-Free Encoder for Language Representation. Transactions of the Association for Computational Linguistics, 10, 73–91. https://doi.org/10.1162/tacl\_a\_00448
  • Elsaid Moussa, M., Hussein Mohamed, E., & Hassan Haggag, M. (2021). Opinion mining: a hybrid framework based on lexicon and machine learning approaches. International Journal of Computers and Applications, 43(8), 786–794. https://doi.org/10.1080/1206212x.2019.1615250
  • Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, And Cybernetics, Part C (Applications and Reviews), 42(4), 463–484. https://doi.org/10.1109/tsmcc.2011.2161285
  • Genc-Nayebi, N., & Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125, 207–219. https://doi.org/10.1016/j.jss.2016.11.027
  • Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision (Issue CS224N).
  • GooglePlay. (2024, ). Applications. https://play.google.com/store/apps?hl=tr
  • Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028
  • He, L., Yang, Z., Lin, H., & Li, Y. (2014). Drug name recognition in biomedical texts: a machine-learning-based method. Drug Discovery Today, 19(5), 610–617. https://doi.org/10.1016/j.drudis.2013.10.006
  • Hearst, M. A., Pedersen, E., Patil, L., Lee, E., Laskowski, P., & Franconeri, S. (2020). An Evaluation of Semantically Grouped Word Cloud Designs. IEEE Transactions on Visualization and Computer Graphics, 26(9), 2748–2761. https://doi.org/10.1109/tvcg.2019.2904683
  • Hippner, H., & Rentzmann, R. (2006). Text Mining. Informatik-Spektrum, 29(4), 287–290. https://doi.org/10.1007/s00287-006-0091-y
  • Huang, C.-H., Yin, J., & Hou, F. (2011). A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method: A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method. Chinese Journal of Computers, 34(5), 856–864. https://doi.org/10.3724/sp.j.1016.2011.00856
  • Jivani, A. G. (2011). A comparative study of stemming algorithms. Int. J. Comp. Tech. Appl., 2(6), 1930–1938.
  • Kang, D., & Park, Y. (2014). Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041–1050. https://doi.org/10.1016/j.eswa.2013.07.101
  • Kayakuş, M., & Yiğit Açıkgöz, F. (2023). Twitter'da Makine Öğrenmesi Yöntemleriyle Sahte Haber Tespiti. Abant Sosyal Bilimler Dergisi, 23(2), 1017–1027. https://doi.org/10.11616/asbi.1266179
  • Latif, S., Rana, R., Qadir, J., Ali, A., Imran, M. A., & Younis, M. S. (2017). Mobile Health in the Developing World: Review of Literature and Lessons From a Case Study. IEEE Access, 5, 11540–11556. https://doi.org/10.1109/access.2017.2710800
  • Loke, R., & Pathak, S. (2023). Decision Support System for Corporate Reputation Based Social Media Listening Using a Cross-Source Sentiment Analysis Engine. Proceedings of the 12th International Conference on Data Science, Technology and Applications, 559–567. https://doi.org/10.5220/0012136400003541
  • McCuiston, V. E., & DeLucenay, A. (2010). Organization Development Quality Improvement Process: Progress Energy's Continuous Business Excellence Initiative. Journal of Business Case Studies (JBCS), 6(6). https://doi.org/10.19030/jbcs.v6i6.255
  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
  • Mostafa, M. M. (2013). More than words: Social networks' text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241–4251. https://doi.org/10.1016/j.eswa.2013.01.019
  • Nguyen, B.-H., & Huynh, V.-N. (2022). Textual analysis and corporate bankruptcy: A financial dictionary-based sentiment approach. Journal of the Operational Research Society, 73(1), 102–121. https://doi.org/10.1080/01605682.2020.1784049
  • Nguyen, N., & Leblanc, G. (2001). Corporate image and corporate reputation in customers' retention decisions in services. Journal of Retailing and Consumer Services, 8(4), 227–236. https://doi.org/10.1016/s0969-6989(00)00029-1
  • O'Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955. https://doi.org/10.1002/asi.20801
  • Ogada, K., Mwangi, W., & Cheruiyot, W. (2015). N-gram based text categorization method for improved data mining. Journal of Information Engineering and Applications, 5(8), 35–43.
  • Pandey, M., Williams, R., Jindal, N., & Batra, A. (2019). Sentiment analysis using lexicon based approach. IITM Journal of Management and IT, 10(1), 68–76.
  • Pejić Bach, M., Krstić, Ž., Seljan, S., & Turulja, L. (2019). Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability, 11(5), 1277. https://doi.org/10.3390/su11051277
  • Peng, Z., & Wan, Y. (2023). Generating business intelligence through automated textual analysis: measuring corporate image with online information. Chinese Management Studies, 17(3), 545–572. https://doi.org/10.1108/cms-07-2021-0318
  • Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
  • Qiang, C. Z., Yamamichi, M., Hausman, V., Altman, D., & Unit, I. (2011). Mobile Applications for the Health Sector_2011. The World Bank.
  • Sial, A. H., Rashdi, S. Y. S., & Khan, A. H. (2021). International Journal of Advanced Trends in Computer Science and Engineering, 10(1), 277–281. https://doi.org/10.30534/ijatcse/2021/391012021
  • Umadevi, M. (2020). Document comparison based on TF-IDF metric. International Research Journal of Engineering and Technology, 7(2), 1546–1550.
  • Wan Min, W. N. S., & Zulkarnain, N. Z. (2020). Comparative Evaluation of Lexicons in Performing Sentiment Analysis. Journal of Advanced Computing Technology and Application, 2(1), 1–8. https://jacta.utem.edu.my/jacta/article/view/5207
  • Waskom, M. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021
  • Weichbroth, P., & Baj-Rogowska, A. (2019). Do online reviews reveal mobile application usability and user experience? The case of WhatsApp. Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, 18, 747–754. https://doi.org/10.15439/2019f289
  • Zhou, H., & Slater, G. W. (2003). A metric to search for relevant words. Physica A: Statistical Mechanics and Its Applications, 329(1–2), 309–327. https://doi.org/10.1016/s0378-4371(03)00625-3

Evaluating of the Impact of Ministry of Health Mobile Applications on Corporate Reputation Through User Comments Using Artificial Intelligence

Year 2024, , 59 - 74, 31.12.2024
https://doi.org/10.17093/alphanumeric.1537174

Abstract

In this study, the impact of mobile applications developed by the Ministry of Health of the Republic of Turkey as part of its digitalization strategy on corporate reputation is analysed by using artificial intelligence methods through user comments. Within the scope of the research, the last 300 user comments of MHRS, Hayat Eve Sığar and eNabız applications on Google Play were analysed, and sentiment analysis and text mining techniques were applied. The findings reveal that MHRS and eNabız applications are generally perceived positively by users, which has a positive impact on the corporate reputation of the Ministry of Health. 81% of MHRS users and 73% of eNabız users made positive comments about the applications. However, for the Hayat Eve Sığar application, the positive comment rate remained at 51 percent, and more technical problems were reported. This shows that the application offers complex user experiences and needs to be improved. In conclusion, it is emphasized that the mobile applications of the Ministry of Health have strengthened its corporate reputation in general, but user satisfaction and sustainability of technical performance are critical to maintaining this reputation.

References

  • Addiga, A., & Bagui, S. (2022). Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency. Journal of Computer and Communications, 10(8), 117–128. https://doi.org/10.4236/jcc.2022.108008
  • Agrawal, R., & Batra, M. (2013). A detailed study on text mining techniques. International Journal of Soft Computing and Engineering, 2(6), 118–121.
  • AppStore. (2024, ). Applications. https://apps.apple.com/tr/developer/t-c-saglik-bakanligi/id867537600?l=tr
  • Bonta, V., Kumaresh, N., & Janardhan, N. (2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology, 8(S2), 1–6. https://doi.org/10.51983/ajcst-2019.8.s2.2037
  • Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1–31.
  • Caropreso, M. F., Matwin, S., & Sebastiani, F. (2001). A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. Text Databases and Document Management: Theory and Practice, 5478(4), 78–102.
  • Clark, J. H., Garrette, D., Turc, I., & Wieting, J. (2022). <scp>Canine</scp>: Pre-training an Efficient Tokenization-Free Encoder for Language Representation. Transactions of the Association for Computational Linguistics, 10, 73–91. https://doi.org/10.1162/tacl\_a\_00448
  • Elsaid Moussa, M., Hussein Mohamed, E., & Hassan Haggag, M. (2021). Opinion mining: a hybrid framework based on lexicon and machine learning approaches. International Journal of Computers and Applications, 43(8), 786–794. https://doi.org/10.1080/1206212x.2019.1615250
  • Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, And Cybernetics, Part C (Applications and Reviews), 42(4), 463–484. https://doi.org/10.1109/tsmcc.2011.2161285
  • Genc-Nayebi, N., & Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125, 207–219. https://doi.org/10.1016/j.jss.2016.11.027
  • Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision (Issue CS224N).
  • GooglePlay. (2024, ). Applications. https://play.google.com/store/apps?hl=tr
  • Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028
  • He, L., Yang, Z., Lin, H., & Li, Y. (2014). Drug name recognition in biomedical texts: a machine-learning-based method. Drug Discovery Today, 19(5), 610–617. https://doi.org/10.1016/j.drudis.2013.10.006
  • Hearst, M. A., Pedersen, E., Patil, L., Lee, E., Laskowski, P., & Franconeri, S. (2020). An Evaluation of Semantically Grouped Word Cloud Designs. IEEE Transactions on Visualization and Computer Graphics, 26(9), 2748–2761. https://doi.org/10.1109/tvcg.2019.2904683
  • Hippner, H., & Rentzmann, R. (2006). Text Mining. Informatik-Spektrum, 29(4), 287–290. https://doi.org/10.1007/s00287-006-0091-y
  • Huang, C.-H., Yin, J., & Hou, F. (2011). A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method: A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method. Chinese Journal of Computers, 34(5), 856–864. https://doi.org/10.3724/sp.j.1016.2011.00856
  • Jivani, A. G. (2011). A comparative study of stemming algorithms. Int. J. Comp. Tech. Appl., 2(6), 1930–1938.
  • Kang, D., & Park, Y. (2014). Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041–1050. https://doi.org/10.1016/j.eswa.2013.07.101
  • Kayakuş, M., & Yiğit Açıkgöz, F. (2023). Twitter'da Makine Öğrenmesi Yöntemleriyle Sahte Haber Tespiti. Abant Sosyal Bilimler Dergisi, 23(2), 1017–1027. https://doi.org/10.11616/asbi.1266179
  • Latif, S., Rana, R., Qadir, J., Ali, A., Imran, M. A., & Younis, M. S. (2017). Mobile Health in the Developing World: Review of Literature and Lessons From a Case Study. IEEE Access, 5, 11540–11556. https://doi.org/10.1109/access.2017.2710800
  • Loke, R., & Pathak, S. (2023). Decision Support System for Corporate Reputation Based Social Media Listening Using a Cross-Source Sentiment Analysis Engine. Proceedings of the 12th International Conference on Data Science, Technology and Applications, 559–567. https://doi.org/10.5220/0012136400003541
  • McCuiston, V. E., & DeLucenay, A. (2010). Organization Development Quality Improvement Process: Progress Energy's Continuous Business Excellence Initiative. Journal of Business Case Studies (JBCS), 6(6). https://doi.org/10.19030/jbcs.v6i6.255
  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
  • Mostafa, M. M. (2013). More than words: Social networks' text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241–4251. https://doi.org/10.1016/j.eswa.2013.01.019
  • Nguyen, B.-H., & Huynh, V.-N. (2022). Textual analysis and corporate bankruptcy: A financial dictionary-based sentiment approach. Journal of the Operational Research Society, 73(1), 102–121. https://doi.org/10.1080/01605682.2020.1784049
  • Nguyen, N., & Leblanc, G. (2001). Corporate image and corporate reputation in customers' retention decisions in services. Journal of Retailing and Consumer Services, 8(4), 227–236. https://doi.org/10.1016/s0969-6989(00)00029-1
  • O'Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955. https://doi.org/10.1002/asi.20801
  • Ogada, K., Mwangi, W., & Cheruiyot, W. (2015). N-gram based text categorization method for improved data mining. Journal of Information Engineering and Applications, 5(8), 35–43.
  • Pandey, M., Williams, R., Jindal, N., & Batra, A. (2019). Sentiment analysis using lexicon based approach. IITM Journal of Management and IT, 10(1), 68–76.
  • Pejić Bach, M., Krstić, Ž., Seljan, S., & Turulja, L. (2019). Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability, 11(5), 1277. https://doi.org/10.3390/su11051277
  • Peng, Z., & Wan, Y. (2023). Generating business intelligence through automated textual analysis: measuring corporate image with online information. Chinese Management Studies, 17(3), 545–572. https://doi.org/10.1108/cms-07-2021-0318
  • Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
  • Qiang, C. Z., Yamamichi, M., Hausman, V., Altman, D., & Unit, I. (2011). Mobile Applications for the Health Sector_2011. The World Bank.
  • Sial, A. H., Rashdi, S. Y. S., & Khan, A. H. (2021). International Journal of Advanced Trends in Computer Science and Engineering, 10(1), 277–281. https://doi.org/10.30534/ijatcse/2021/391012021
  • Umadevi, M. (2020). Document comparison based on TF-IDF metric. International Research Journal of Engineering and Technology, 7(2), 1546–1550.
  • Wan Min, W. N. S., & Zulkarnain, N. Z. (2020). Comparative Evaluation of Lexicons in Performing Sentiment Analysis. Journal of Advanced Computing Technology and Application, 2(1), 1–8. https://jacta.utem.edu.my/jacta/article/view/5207
  • Waskom, M. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021
  • Weichbroth, P., & Baj-Rogowska, A. (2019). Do online reviews reveal mobile application usability and user experience? The case of WhatsApp. Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, 18, 747–754. https://doi.org/10.15439/2019f289
  • Zhou, H., & Slater, G. W. (2003). A metric to search for relevant words. Physica A: Statistical Mechanics and Its Applications, 329(1–2), 309–327. https://doi.org/10.1016/s0378-4371(03)00625-3
There are 40 citations in total.

Details

Primary Language English
Subjects Management Information Systems, Operations Research
Journal Section Articles
Authors

Mehmet Kayakuş 0000-0003-0394-5862

Publication Date December 31, 2024
Submission Date August 22, 2024
Acceptance Date December 4, 2024
Published in Issue Year 2024

Cite

APA Kayakuş, M. (2024). Evaluating of the Impact of Ministry of Health Mobile Applications on Corporate Reputation Through User Comments Using Artificial Intelligence. Alphanumeric Journal, 12(2), 59-74. https://doi.org/10.17093/alphanumeric.1537174

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